Spatially-Aligned Chroma from Luma Prediction for Lossless JPEG XS Raw Image Compression

Abstract

This study proposes a Chroma from Luma (CfL)-enhanced Star-Tetrix transform (STT), referred to as CfL-STT, for improving raw image compression in JPEG XS. The proposed CfL-STT integrates CfL prediction into the STT to predict chroma components from the luma component in CFA-sampled raw images. Unlike conventional CfL prediction designed for full-color images, the proposed method employs spatially aligned luma samples obtained via linear interpolation along the horizontal and vertical directions to match the chroma sampling grid. This spatial alignment suppresses high-frequency sensor noise while preserving cross-channel correlation, resulting in a more decorrelated Y-Delta-Du-Dv color space. The proposed method was implemented in the JPEG XS reference software and evaluated on raw image datasets. Experimental results demonstrate that a direct application of CfL prediction yields image-dependent performance and may degrade coding efficiency due to the lack of spatial alignment, whereas the proposed CfL-STT consistently improves coding efficiency in lossless raw image compression while preserving exact reversibility.